Microseismic Event Recognition and Transfer Learning Based on Convolutional Neural Network and Attention Mechanisms
Jin Shu, Zhang Shi-chao, Gao Ya, Yu Ben-li, Zhen Sheng-lai*
1. Information Materials and Intelligent Sensing Laboratory of Anhui Province, Anhui University, Hefei, 230601, China
2. Key Laboratory of Opto-Electronic Information Acquisition and Manipulation, Ministry of Education, Anhui University, Hefei, 230601, China
3. School of Public Safety and Emergency Management, Anhui University of Science and Technology, Huainan, 232000, China
4. IDETECK CO., LTD., Chuangxin Avenue, Hefei 230601, Anhui, China
Abstract Microseismic monitoring technology is widely used in tunnel and coal mine safety production. For signals generated by ultra-weak microseismic events, traditional sensors encounter limitations in terms of detection sensitivity. Given the complex engineering environment, automatic multi-classification of microseismic data is highly required. In this study, we use acceleration sensors to collect signals and combine the improved Visual Geometry Group with a convolutional block attention module to obtain a new network structure, termed CNN_BAM, for automatic classification and identification of microseismic events. We use the dataset collected from the Hanjiang-to-Weihe River Diversion Project to train and validate the network model. Results show that the CNN_BAM model exhibits good feature extraction ability, achieving a recognition accuracy of 99.29%, surpassing all its counterparts. The stability and accuracy of the classification algorithm improve remarkably. In addition, through fine-tuning and migration to the Pan II Mine Project, the network demonstrates reliable generalization performance. This outcome reflects its adaptability across diff erent projects and promising application prospects.
Fund: This work was supported by the Key Research a n d Development Plan of Anhui Province (202104a05020059) and the Excellent Scientific Research and Innovation Team of Anhui Province (2022AH010003). Partial financial support from Hefei Comprehensive National Science Center is highly appreciated.
About author: Shu Jin is currently working toward her MS degree in optical engineering in the School of Physics and optoelectronic engineering, Anhui University, Hefei, China. She is a master’s candidate directed by Prof. Shenglai Zhen.
Cite this article:
. Microseismic Event Recognition and Transfer Learning Based on Convolutional Neural Network and Attention Mechanisms[J]. APPLIED GEOPHYSICS, 2025, 22(4): 1220-1232.
2025, Vol. 22 
